TerraSenseTK: a toolkit for remote soil nutrient estimation

Intensive farming endangers soil quality in various ways. Researchers show that if these practices continue, humanity will be faced with food production issues. For this matter, Earth Observation, more concretely Soil Sensing, along with Machine Learning, can be employed to monitor several indicators of soil degradation, such as soil salinity, soil heavy metal contamination and soil nutrients estimation. More concretely, Soil Nutrients are of great importance. For instance, to understand which crop better suits the land, the soil nutrients must be identified. However, sampling soil is a laborous and expensive task, which can be leveraged by Remote Sensing and Machine Learning. Several studies have already been developed in this matter, although many gaps still exist. Among them, the lack of cross-dataset evaluations of existing algorithms, and also the steep learning curve to the Earth Observation domain that prevents many researchers from embracing this field. In this sense, we propose TerraSense ToolKit (TSTK), a python toolkit that addresses these challenges. In this work, the possibility to use Remote sensing along with Machine Learning algorithms to per form Soil Nutrient Estimation is explored, additionally, a nutrient estimation toolkit is proposed, and the effectivity of it is tested in a soil nutrient estimation case study. This toolkit is capable of simplifying Remote Sensing experiments and aims at reducing the barrier to entry to the field of Earth Observation. It comes with a preconfigured case study which implements a soil sensing pipeline. To evaluate the usability of the toolkit, experiments with five different crops were executed, namely with Wheat, Barley, Maize, Sunflower and Vineyards. This case study gave visibility to an underlying unbalanced data problem, which is not well addressed in the current State of the Art.A agricultura intensiva poe em perigo a qualidade do solo de v ˜ arias formas. Os investigadores ´ mostram que, se continuarmos com estas praticas, a humanidade ser ´ a confrontada com quest ´ oes de ˜ produc¸ao alimentar. Para este efeito, a Observac¸ ˜ ao da Terra, mais concretamente o Sensoriamento ˜ do Solo, juntamente com a aprendizagem automatica, podem ser utilizadas para monitorizar v ´ arios ´ indicadores da degradac¸ao do solo, tais como a salinidade do solo, a contaminac¸ ˜ ao do solo por metais ˜ pesados e a quantificac¸ao dos nutrientes do solo. Mais concretamente, os Nutrientes do Solo s ˜ ao de ˜ grande importancia. Por exemplo para compreender qual a cultura que melhor se adapta ao solo, os ˆ nutrientes do solo devem ser identificados. No entanto, a amostragem do solo e uma tarefa trabalhosa ´ e dispendiosa, que pode ser impulsionada pela percepc¸ao remota e pela aprendizagem autom ˜ atica. ´ Ja foram desenvolvidos v ´ arios estudos sobre este assunto, embora ainda existam muitas lacunas. ´ Entre eles, a falta de avaliac¸oes cruzadas dos algoritmos existentes, e tamb ˜ em a curva de aprendiza- ´ gem acentuada para o campo de Observac¸ao da Terra que impede muitos investigadores de enveredar ˜ por este campo. Neste sentido, propomos TSTK, um toolkit em python que aborda estes desafios. Neste trabalho, e explorada a possibilidade de usar a Percepc¸ ´ ao Remota juntamente com os algo- ˜ ritmos de Aprendizagem Automatica para realizar a Estimativa de Nutrientes do Solo. Al ´ em disso, ´ e´ proposto um toolkit de estimativa de nutrientes e tambem um pipeline para o devido efeito, a efetividade ´ do toolkit e testada num caso de estudo de Estimac¸ ´ ao de Nutrientes no Solo. ˜ Este toolkit e capaz de simplificar as experi ´ encias de Percepc¸ ˆ ao Remota e visa reduzir a barreira ˜ de entrada no campo da Observac¸ao da Terra. Para avaliar a usabilidade do toolkit, foram executadas ˜ experiencias com cinco culturas diferentes, nomeadamente Trigo, Cevada, Milho, Girassol e Vinha. Este ˆ caso de estudo deu visibilidade a um problema subjacente de dados desiquilibrados, o qual nao˜ e bem ´ identificado no Estado da Arte atual

Machine learning to generate soil information

This thesis is concerned with the novel use of machine learning (ML) methods in soil science research. ML adoption in soil science has increased considerably, especially in pedometrics (the use of quantitative methods to study the variation of soils). In parallel, the size of the soil datasets has also increased thanks to projects of global impact that aim to rescue legacy data or new large extent surveys to collect new information. While we have big datasets and global projects, currently, modelling is mostly based on "traditional" ML approaches which do not take full advantage of these large data compilations. This compilation of these global datasets is severely limited by privacy concerns and, currently, no solution has been implemented to facilitate the process. If we consider the performance differences derived from the generality of global models versus the specificity of local models, there is still a debate on which approach is better. Either in global or local DSM, most applications are static. Even with the large soil datasets available to date, there is not enough soil data to perform a fully-empirical, space-time modelling. Considering these knowledge gaps, this thesis aims to introduce advanced ML algorithms and training techniques, specifically deep neural networks, for modelling large datasets at a global scale and provide new soil information. The research presented here has been successful at applying the latest advances in ML to improve upon some of the current approaches for soil modelling with large datasets. It has also created opportunities to utilise information, such as descriptive data, that has been generally disregarded. ML methods have been embraced by the soil community and their adoption is increasing. In the particular case of neural networks, their flexibility in terms of structure and training makes them a good candidate to improve on current soil modelling approaches

Explainable machine learning in soil mapping: Peeking into the black box

Während des Anthropozäns und insbesondere in den letzten Jahrzehnten hat sich die Umwelt der Erde stark verändert. Die planetarischen Grenzen stehen zunehmend unter Druck. Da der Boden als wichtiger Teil der Kohlenstoff- und Stickstoffkreisläufe das Klima beeinflusst, ist er eine wichtige Ressource bei der Bewältigung dieser Umweltprobleme. Folglich spielt das Wissen über den Boden, Bodenprozesse und Bodenfunktionen eine wesentliche Rolle bei der Erforschung und Lösung dieser schwerwiegenden ökologischen und sozioökonomischen Herausforderungen. Die Kartierung und Modellierung des Bodens liefert räumliche Kenntnis über den Zustand des Bodens und seine Veränderungen im Laufe der Zeit. Dies ermöglicht es, Methoden der Bodenbewirtschaftung und Lösungsansätze für Umweltprobleme zu beurteilen und zu bewerten. Methoden des maschinellen Lernens haben sich für die räumliche Kartierung und Modellierung des Bodens als geeignet erwiesen. Oft handelt es sich dabei aber um Black Boxes und die Modellentscheidungen und -ergebnisse werden nicht erklärt. Allerdings würden erklärbare Bodenmodelle auf der Grundlage des maschinellen Lernens die Erkennung von Umweltveränderungen erleichtern, zur Entscheidungsfindung für den Umweltschutz beitragen und die Akzeptanz von Wissenschaft, Politik in Gesellschaft fördern. Daher sind die jüngsten Bemühungen im Bereich des maschinellen Lernens darauf ausgerichtet, den konventionellen Rahmen des maschinellen Lernens auf er¬klärbares maschinelles Lernen zu erweitern, um 1) Entscheidungen zu begründen, 2) die Modelle besser zu steuern und 3) zu verbessern und 4) neues Wissen zu generieren. Die Kernelemente für erklärbares maschinelles Lernen sind Transparenz, Interpretierbarkeit und Erklärbarkeit. Darüber hinaus sind domain knowledge und wissenschaftliche Konsistenz entscheidend. Bei der Bodenmodellierung spielten die Konzepte des erklärbaren maschinellen Lernens jedoch bisher eine geringe Rolle. Ziel dieser Arbeit war es, zu untersuchen und zu beschreiben, wie Transparenz, Interpretierbarkeit und Erklärbarkeit im Rahmen der Bodenmodellierung erreicht werden können. Die Fallbeispiele zeigten, wie Konsistenz mit Modellvergleichen bewertet werden kann und domain knowledge in die Modelle einfließt. Ebenso zeigten die Studien, wie Transparenz mit reproduzierbarer Proben- und Variablenauswahl erreicht werden kann und wie die Interpretation der Modelle mit domain knowledge verknüpft werden kann, um die Modellergebnisse besser zu erklären und in Bezug zu bodenkundlichem Wissen zu setzen sind.During the Anthropocene and especially in the past decades earth’s environment has undergone major changes. The planetary boundaries are increasingly under pressure. Since soil affects climate as compartment of the carbon and nitrogen cycles, it is an important resource in approaching these environmental problems. Consequently, knowledge about soil, soil processes and soil functions plays an essential role in research on and solutions for these severe environmental and socio-economic challenges. The mapping and modelling of soil provides spatial knowledge of soil status and changes over time, which allows to assess and evaluate soil management practices and attempts to solve to environmental problems. Machine learning methods have proven to be suitable for spatial mapping and modelling of soil, but often are black boxes and the model decisions and prediction results remain unexplained. However, explainable soil models based on machine learning would facilitate detection of environmental changes, contribute to decision making for environmental protection and foster acceptance in science, politics, and society. Therefore, latest efforts in machine learning were to expand the conventional machine learning framework to explainable machine learning to 1) justify decisions, 2) control, and 3) improve models and 4) to discover new knowledge. The core elements for explainable machine learning are transparency, interpretability and explainability. Additionally, domain knowledge and scientific consistency are crucial. However, to date the concepts of explainable machine learning played a marginal role in soil modelling and mapping. Objective of this thesis was to explore and describe how transparency, interpretability and explainability can be achieved in the soil mapping framework. The example studies showed how scientific consistency can be evaluated with model comparison and domain knowledge was and incorporated in DSM models. The studies showed how transparency can be accomplished with reproducible sample and covariate selection, and how interpretation of the models can be linked with domain knowledge about soil formation and processes to explain the model results

Relationships in soil distribution from digital soil modelling and mapping over eastern Australia under past, present and future conditions

This research project applied digital soil modelling and mapping (DSMM) techniques to elucidate relationships between key soil properties and the main soil-forming factors. It attempted to address several broad research issues relating to quantifying the factors that control soil distribution and identifying how these combine together to control soil distribution and their change due to alteration in land use and climate over New South Wales and eastern Australia.«br /» «br /» These broad issues were examined through a number of more specific research issues that were progressively addressed over five chapters, each intended as publishable journal papers. These chapters/journal papers relate to (i) the influence of lithology in soil formation and its application in DSMM (ii) relationships of soil-forming factors to key soil properties and their use in digital soil mapping; (iii) factors controlling the distribution of soil organic carbon stocks (SOC), spatially and with depth; (iv) change in SOC stocks following historic clearing of native vegetation, and (v) change in SOC stocks with projected climate change.«br /» «br /» The strong influence of lithology in controlling soil distribution was demonstrated. Following its classification into 12 classes based on mineral and chemical composition, it was shown to have the highest influence of all soil-forming factors for six key soil properties (SOC, pH, cation exchange capacity (CEC), sum-of-bases, total phosphorous and clay content) examined over NSW. Lithology had similar influence at the scale of eastern Australia; however climate variables were of equivalent or slightly stronger influence for SOC and pH. It was shown to have two to five times more influence than the next highest ranked geophysical covariate such as gamma radiometrics in the models. A marked improvement in the statistical quality of digital models and maps was demonstrated when lithology was applied together with other geophysical covariates.«br /» «br /» Quantitative relationships that are readily interpreted were developed with eight key properties (those listed above plus sand and silt contents) over eastern Australia. These relationships at least partially solve Jenny’s fundamental soil equation in a manner that is more universally applicable and readily interpreted than appears to have been reported previously. Using these relationships, the quantitative influence of the different factors on each soil property is determined, including the unit change per unit variation in the factor, for example a decrease of 0.11 pH units for each 100 mm increase in annual rainfall for the 0-10 cm interval (other factors remaining constant). These relationships were applied together with readily available covariate grids to prepare digital soil maps (DSMs) with 100-m resolution for the eight soil properties over NSW. The predictive ability demonstrated by the maps was broadly moderate, with Lin’s concordance generally between 0.4 and 0.7. They compared well with maps prepared using more sophisticated modelling methods and covariate data. They have the ability to be readily prepared and interpreted and thus have the potential to serve as a useful introduction to the more sophisticated DSMM approaches.«br /» «br /» Systematic patterns of SOC stock levels were graphically demonstrated over 45 different climate-parent material-vegetation cover regimes for upper soils (0-30 cm) and lower soils (30-100 cm) over eastern Australia. There are generally uniform trends of increasing SOC stocks with increasingly moist climate, increasing mafic character of parent material and increasing vegetation cover. Average SOC stocks in the 0-30 cm depth interval range from 16.3 Mg ha-1 (t/ha) in dry, highly siliceous parent material and low vegetation cover environments, up to over 145.0 Mg ha-1 in wet, mafic parent material and high vegetation cover environments. It was demonstrated that the proportion of SOC stored in the subsoil (30-100 cm) relative to the top 100 cm varies systematically from an average of 43% in moist climates to an average of 54% in dry climates.«br /» «br /» Digital soil maps of pre-clearing (pre-European) SOC stocks (100-m resolution) were prepared over NSW. These maps may be used to provide baseline soil carbon levels for carbon turnover models and carbon accounting and trading schemes. They were demonstrated to outperform the existing equivalent maps produced by conventional soil survey methods, with independent validation RMSE values being 33% lower. Comparison of these maps with current SOC stock maps allowed an examination of the change in SOC over NSW following native vegetation clearing. A total SOC loss of approximately 0.53 Gt (530 million Mg or tonnes), or 12.6% over the entire State was revealed. It was demonstrated that the change in SOC stocks following clearing increases (in both absolute and relative terms) with increasingly cool (moist) climate, more mafic parent material and more intensive land use. In the 56 different climate-parent material – land use regimes, the loss varied from less than 1 Mg ha-1 (or 4%) in warmer climates over highly siliceous parent materials under grazing land uses to 44.3 Mg ha-1 (or 50.0%) in cooler (moist) conditions over mafic parent materials under intensive cropping land use.«br /» «br /» Digital soil mapping techniques involving Cubist piecewise linear decision trees, in combination with a space-for-time substitution process (DSM-SFTS), were demonstrated to be effective in mapping the potential change in SOC stocks due to projected climate change over NSW until approximately 2070. Considerable variation in both direction and magnitude of change was demonstrated with application of the 12 different climate change models with their differing climate trajectories. For the mean state-wide change there were some climate models that predicted an increase but others that predicted a decrease over the two depth intervals studied (0-30 and 30-100 cm). Greater consistency between climate change models is required. The predicted SOC changes are primarily controlled by the balance between changing temperatures and rainfall. However, the extent of change is also shown to be dependent on the precise environmental regime, with systematically differing changes demonstrated over 36 current climate-parent material-land use combinations. For example, the projected mean decline of SOC is less than 1 Mg ha-1 for dry-highly siliceous-cropping regimes but over 15 Mg ha-1 for wet-mafic-native vegetation regimes.«br /» «br /» The study has provided quantitative data on the influence of the main soil-forming factors. The necessity of considering the combined influence of multiple soil-forming factors to make meaningful quantitative estimates of current and potential future soil properties is demonstrated. Clear patterns of soil property distribution and change under changing land use and climate conditions are identified, particularly for the vital soil property of SOC. The presentation of relationships that are readily interpreted can assist in their application in natural resource planning and management activities and also in other environment modelling programs. They may thus potentially help to address a range of environmental challenges facing eastern Australia and beyond

Developing models for the data-based mechanistic approach to systems analysis:Increasing objectivity and reducing assumptions

Stochastic State-Space Time-Varying Random Walk models have been developed, allowing the existing Stochastic State Space models to operate directly on irregularly sampled time-series. These TVRW models have been successfully applied to two different classes of models benefiting each class in different ways. The first class of models - State Dependent Parameter (SDP) models and used to investigate the dominant dynamic modes of nonlinear dynamic systems and the non-linearities in these models affected by arbitrary State Variables. In SDP locally linearised models it is assumed that the parameters that describe system’s behaviour changes are dependent upon some aspect of the system (it’s ‘state’). Each parameter can be dependent on one or more states. To estimate the parameters that are changing at a rate related to that of it’s states, the estimation procedure is conducted in the state-space along the potentially multivariate trajectory of the states which drive the parameters. The introduction of the newly developed TVRW models significantly improves parameter estimation, particularly in data rich neighbourhoods of the state-space when the parameter is dependent on more than one state, and the ends of the data-series when the parameter is dependent on one state with few data points. The second class of models are known as Dynamic Harmonic Regression (DHR) models and are used to identify the dominant cycles and trends of time-series. DHR models the assumption is that a signal (such as a time-series) can be broken down into four (unobserved) components occupying different parts of the spectrum: trend, seasonal cycle, other cycles, and a high frequency irregular component. DHR is confined to uniformly sampled time-series. The introduction of the TVRW models allows DHR to operate on irregularly sampled time-series, with the added benefit of forecasting origin no longer being confined to starting at the end of the time-series but can now begin at any point in the future. Additionally, the forecasting sampling rate is no longer limited to the sampling rate of the time-series. Importantly, both classes of model were designed to follow the Data-Based Mechanistic (DBM) approach to modelling environmental systems, where the model structure and parameters are to be determined by the data (Data-Based) and then the subsequent models are to be validated based on their physical interpretation (Mechanistic). The aim is to remove the researcher’s preconceptions from model development in order to eliminate any bias, and then use the researcher’s knowledge to validate the models presented to them. Both classes of model lacked model structure identification procedures and so model structure was determined by the researcher, against the DBM approach. Two different model structure identification procedures, one for SDP and the other for DHR, were developed to bring both classes of models back within the DBM framework. These developments have been presented and tested here on both simulated data and real environmental data, demonstrating their importance, benefits and role in environmental modelling and exploratory data analysis

Sustainable Agriculture and Soil Conservation

Soil degradation is one of the most topical environmental threats. A number of processes causing soil degradation, specifically erosion, compaction, salinization, pollution, and loss of both organic matter and soil biodiversity, are also strictly connected to agricultural activity and its intensification. The development and adoption of sustainable agronomic practices able to preserve and enhance the physical, chemical, and biological properties of soils and improve agroecosystem functions is a challenge for both scientists and farmers. The Special Issue entitled “Sustainable Agriculture and Soil Conservation” collects 12 original contributions addressing the state of the art of sustainable agriculture and soil conservation. The papers cover a wide range of topics, including organic agriculture, soil amendment and soil organic carbon (SOC) management, the impact of SOC on soil water repellency, the effects of soil tillage on the quantity of SOC associated with several fractions of soil particles and depth, and SOC prediction, using visible and near-infrared spectra and multivariate modeling. Moreover, the effects of some soil contaminants (e.g., crude oil, tungsten, copper, and polycyclic aromatic hydrocarbons) are discussed or reviewed in light of the recent literature. The collection of the manuscripts presented in this Special Issue provides a relevant knowledge contribution for improving our understanding on sustainable agriculture and soil conservation, thus stimulating new views on this main topic

Agroforestry Opportunities for Enhancing Resilience to Climate Change in Rainfed Areas,

Not AvailableAgroforestry provides a unique opportunity to achieve the objectives of enhancing the productivity and improving the soil quality. Tree systems can also play an important role towards adapting to the climate variability and important carbon sinks which helps to decrease the pressure on natural forests. Realizing the importance of the agroforestry in meeting the twin objectives of mitigation and adaptation to climate change as well as making rainfed agriculture more climate resilient, the ICAR-CRIDA has taken up the challenge in pursuance of National Agroforestry Policy 2014, in preparing a book on Agroforestry Opportunities for Enhancing Resilience to Climate Change in Rainfed Areas at ICAR-CRIDA to sharpen the skills of all stakeholders at national, state and district level in rainfed areas to increase agricultural productivity in response to climate changeNot Availabl

Data-driven model development in environmental geography - Methodological advancements and scientific applications

Die Erfassung räumlich kontinuierlicher Daten und raum-zeitlicher Dynamiken ist ein Forschungsschwerpunkt der Umweltgeographie. Zu diesem Ziel sind Modellierungsmethoden erforderlich, die es ermöglichen, aus limitierten Felddaten raum-zeitliche Aussagen abzuleiten. Die Komplexität von Umweltsystemen erfordert dabei die Verwendung von Modellierungsstrategien, die es erlauben, beliebige Zusammenhänge zwischen einer Vielzahl potentieller Prädiktoren zu berücksichtigen. Diese Anforderung verlangt nach einem Paradigmenwechsel von der parametrischen hin zu einer nicht-parametrischen, datengetriebenen Modellentwicklung, was zusätzlich durch die zunehmende Verfügbarkeit von Geodaten verstärkt wird. In diesem Zusammenhang haben sich maschinelle Lernverfahren als ein wichtiges Werkzeug erwiesen, um Muster in nicht-linearen und komplexen Systemen zu erfassen. Durch die wachsende Popularität maschineller Lernverfahren in wissenschaftlichen Zeitschriften und die Entwicklung komfortabler Softwarepakete wird zunehmend der Fehleindruck einer einfachen Anwendbarkeit erzeugt. Dem gegenüber steht jedoch eine Komplexität, die im Detail nur durch eine umfassende Methodenkompetenz kontrolliert werden kann. Diese Problematik gilt insbesondere für Geodaten, die besondere Merkmale wie vor allem räumliche Abhängigkeit aufweisen, womit sie sich von "gewöhnlichen" Daten abheben, was jedoch in maschinellen Lernanwendungen bisher weitestgehend ignoriert wird. Die vorliegende Arbeit beschäftigt sich mit dem Potenzial und der Sensitivität des maschinellen Lernens in der Umweltgeographie. In diesem Zusammenhang wurde eine Reihe von maschinellen Lernanwendungen in einem breiten Spektrum der Umweltgeographie veröffentlicht. Die einzelnen Beiträge stehen unter der übergeordneten Hypothese, dass datengetriebene Modellierungsstrategien nur dann zu einem Informationsgewinn und zu robusten raum-zeitlichen Ergebnissen führen, wenn die Merkmale von geographischen Daten berücksichtigt werden. Neben diesem übergeordneten methodischen Fokus zielt jede Anwendung darauf ab, durch adäquat angewandte Methoden neue fachliche Erkenntnisse in ihrem jeweiligen Forschungsgebiet zu liefern. Im Rahmen der Arbeit wurde eine Vielzahl relevanter Umweltmonitoring-Produkte entwickelt. Die Ergebnisse verdeutlichen, dass sowohl hohe fachwissenschaftliche als auch methodische Kenntnisse unverzichtbar sind, um den Bereich der datengetriebenen Umweltgeographie voranzutreiben. Die Arbeit demonstriert erstmals die Relevanz räumlicher Überfittung in geographischen Lernanwendungen und legt ihre Auswirkungen auf die Modellergebnisse dar. Um diesem Problem entgegenzuwirken, wird eine neue, an Geodaten angepasste Methode zur Modellentwicklung entwickelt, wodurch deutlich verbesserte Ergebnisse erzielt werden können. Diese Arbeit ist abschließend als Appell zu verstehen, über die Standardanwendungen der maschinellen Lernverfahren hinauszudenken, da sie beweist, dass die Anwendung von Standardverfahren auf Geodaten zu starker Überfittung und Fehlinterpretation der Ergebnisse führt. Erst wenn Eigenschaften von geographischen Daten berücksichtigt werden, bietet das maschinelle Lernen ein leistungsstarkes Werkzeug, um wissenschaftlich verlässliche Ergebnisse für die Umweltgeographie zu liefern

Multi-sensor and data fusion approach for determining yield limiting factors and for in-situ measurement of yellow rust and fusarium head blight in cereals

The world’s population is increasing and along with it, the demand for food. A novel parametric model (Volterra Non-linear Regressive with eXogenous inputs (VNRX)) is introduced for quantifying influences of individual and multiple soil properties on crop yield and normalised difference vegetation Index. The performance was compared to a random forest method over two consecutive years, with the best results of 55.6% and 52%, respectively. The VNRX was then implemented using high sampling resolution soil data collected with an on-line visible and near infrared (vis-NIR) spectroscopy sensor predicting yield variation of 23.21%. A hyperspectral imager coupled with partial least squares regression was successfully applied in the detection of fusarium head blight and yellow rust infection in winter wheat and barley canopies, under laboratory and on-line measurement conditions. Maps of the two diseases were developed for four fields. Spectral indices of the standard deviation between 500 to 650 nm, and the squared difference between 650 and 700 nm, were found to be useful in differentiating between the two diseases, in the two crops, under variable water stress. The optimisation of the hyperspectral imager for field measurement was based on signal-to-noise ratio, and considered; camera angle and distance, integration time, and light source angle and distance from the crop canopy. The study summarises in the proposal of a new method of disease management through suggested selective harvest and fungicide applications, for winter wheat and barley which theoretically reduced fungicide rate by an average of 24% and offers a combined saving of the two methods of £83 per hectare

Operational progression of digital soil assessment for agricultural growth in Tasmania, Australia

Tasmania, Australia, is currently undergoing a period of agricultural expansion through the development of new irrigation schemes across the State, primarily to stimulate the economy and ensure future food security. ‘Operational Progression of Digital Soil Assessment (DSA) for Agricultural Growth in Tasmania, Australia’ presents the adaptation and operationalisation of quantitative approaches for regional land evaluation within these schemes, specifically applied Digital Soil Mapping (DSM) to inform a land suitability evaluation for 20 different agricultural crops, and ultimately a spatial indication of the State’s agricultural versatility and capital. DSM had not previously been applied or tested in Tasmania; the research examines and validates DSM approaches with respect to the State’s unique and complex soils and biophysical interactions with climate and terrain, and how these apply to various agricultural land uses. The thesis is a major contribution to the methodology and development of one of the first major operational DSA programs in Australia, and forms a framework for this type of DSM approach to be used in future operational land evaluation elsewhere